Pandas Profiling Report

Dataset statistics

Number of variables	29
Number of observations	2240
Missing cells	24
Missing cells (%)	< 0.1%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	507.6 KiB
Average record size in memory	232.1 B

Variable types

Numeric	15
Categorical	14

Warnings

`Z_CostContact` has constant value "3"	Constant
`Z_Revenue` has constant value "11"	Constant
`Dt_Customer` has a high cardinality: 663 distinct values	High cardinality
`Income` is highly correlated with `MntWines` and 4 other fields	High correlation
`Kidhome` is highly correlated with `NumCatalogPurchases`	High correlation
`MntWines` is highly correlated with `Income` and 4 other fields	High correlation
`MntFruits` is highly correlated with `MntMeatProducts` and 2 other fields	High correlation
`MntMeatProducts` is highly correlated with `Income` and 6 other fields	High correlation
`MntFishProducts` is highly correlated with `MntFruits` and 3 other fields	High correlation
`MntSweetProducts` is highly correlated with `MntFruits` and 2 other fields	High correlation
`NumWebPurchases` is highly correlated with `MntWines` and 1 other fields	High correlation
`NumCatalogPurchases` is highly correlated with `Income` and 6 other fields	High correlation
`NumStorePurchases` is highly correlated with `Income` and 3 other fields	High correlation
`NumWebVisitsMonth` is highly correlated with `Income` and 2 other fields	High correlation
`Income` is highly correlated with `Kidhome` and 10 other fields	High correlation
`Kidhome` is highly correlated with `Income` and 4 other fields	High correlation
`MntWines` is highly correlated with `Income` and 9 other fields	High correlation
`MntFruits` is highly correlated with `Income` and 7 other fields	High correlation
`MntMeatProducts` is highly correlated with `Income` and 9 other fields	High correlation
`MntFishProducts` is highly correlated with `Income` and 7 other fields	High correlation
`MntSweetProducts` is highly correlated with `Income` and 7 other fields	High correlation
`MntGoldProds` is highly correlated with `Income` and 8 other fields	High correlation
`NumWebPurchases` is highly correlated with `Income` and 5 other fields	High correlation
`NumCatalogPurchases` is highly correlated with `Income` and 10 other fields	High correlation
`NumStorePurchases` is highly correlated with `Income` and 9 other fields	High correlation
`NumWebVisitsMonth` is highly correlated with `Income` and 1 other fields	High correlation
`Income` is highly correlated with `MntWines` and 3 other fields	High correlation
`Kidhome` is highly correlated with `NumCatalogPurchases`	High correlation
`MntWines` is highly correlated with `Income` and 4 other fields	High correlation
`MntFruits` is highly correlated with `MntMeatProducts` and 2 other fields	High correlation
`MntMeatProducts` is highly correlated with `Income` and 7 other fields	High correlation
`MntFishProducts` is highly correlated with `MntFruits` and 3 other fields	High correlation
`MntSweetProducts` is highly correlated with `MntFruits` and 2 other fields	High correlation
`NumWebPurchases` is highly correlated with `MntWines` and 2 other fields	High correlation
`NumCatalogPurchases` is highly correlated with `Income` and 5 other fields	High correlation
`NumStorePurchases` is highly correlated with `Income` and 4 other fields	High correlation
`Income` is highly correlated with `NumWebVisitsMonth` and 8 other fields	High correlation
`NumWebVisitsMonth` is highly correlated with `Income` and 3 other fields	High correlation
`NumStorePurchases` is highly correlated with `Income` and 10 other fields	High correlation
`AcceptedCmp4` is highly correlated with `MntWines`	High correlation
`MntMeatProducts` is highly correlated with `Income` and 3 other fields	High correlation
`NumCatalogPurchases` is highly correlated with `Income` and 5 other fields	High correlation
`Kidhome` is highly correlated with `NumStorePurchases` and 3 other fields	High correlation
`MntWines` is highly correlated with `Income` and 9 other fields	High correlation
`MntFruits` is highly correlated with `Income` and 4 other fields	High correlation
`Teenhome` is highly correlated with `NumDealsPurchases`	High correlation
`NumWebPurchases` is highly correlated with `NumStorePurchases` and 3 other fields	High correlation
`MntFishProducts` is highly correlated with `Income` and 6 other fields	High correlation
`AcceptedCmp1` is highly correlated with `Income` and 1 other fields	High correlation
`AcceptedCmp5` is highly correlated with `Income` and 2 other fields	High correlation
`NumDealsPurchases` is highly correlated with `NumWebVisitsMonth` and 2 other fields	High correlation
`MntGoldProds` is highly correlated with `NumWebVisitsMonth` and 4 other fields	High correlation
`MntSweetProducts` is highly correlated with `Kidhome` and 4 other fields	High correlation
`AcceptedCmp1` is highly correlated with `Z_Revenue` and 1 other fields	High correlation
`Response` is highly correlated with `Z_Revenue` and 1 other fields	High correlation
`AcceptedCmp4` is highly correlated with `Z_Revenue` and 1 other fields	High correlation
`AcceptedCmp5` is highly correlated with `Z_Revenue` and 1 other fields	High correlation
`AcceptedCmp2` is highly correlated with `Z_Revenue` and 1 other fields	High correlation
`Complain` is highly correlated with `Z_Revenue` and 1 other fields	High correlation
`Z_Revenue` is highly correlated with `AcceptedCmp1` and 11 other fields	High correlation
`AcceptedCmp3` is highly correlated with `Z_Revenue` and 1 other fields	High correlation
`Z_CostContact` is highly correlated with `AcceptedCmp1` and 11 other fields	High correlation
`Education` is highly correlated with `Z_Revenue` and 1 other fields	High correlation
`Kidhome` is highly correlated with `Z_Revenue` and 1 other fields	High correlation
`Marital_Status` is highly correlated with `Z_Revenue` and 1 other fields	High correlation
`Teenhome` is highly correlated with `Z_Revenue` and 1 other fields	High correlation
`Income` has 24 (1.1%) missing values	Missing
`ID` has unique values	Unique
`Recency` has 28 (1.2%) zeros	Zeros
`MntFruits` has 400 (17.9%) zeros	Zeros
`MntFishProducts` has 384 (17.1%) zeros	Zeros
`MntSweetProducts` has 419 (18.7%) zeros	Zeros
`MntGoldProds` has 61 (2.7%) zeros	Zeros
`NumDealsPurchases` has 46 (2.1%) zeros	Zeros
`NumWebPurchases` has 49 (2.2%) zeros	Zeros
`NumCatalogPurchases` has 586 (26.2%) zeros	Zeros

Reproduction

Analysis started	2021-12-05 16:49:21.687371
Analysis finished	2021-12-05 16:50:24.503957
Duration	1 minute and 2.82 seconds
Software version	pandas-profiling v3.0.0
Download configuration	config.json

ID
Real number (ℝ_≥0)

UNIQUE

Distinct	2240
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	5592.159821

Minimum	0
Maximum	11191
Zeros	1
Zeros (%)	< 0.1%
Negative	0
Negative (%)	0.0%
Memory size	17.6 KiB

Quantile statistics

Minimum	0
5-th percentile	576.85
Q1	2828.25
median	5458.5
Q3	8427.75
95-th percentile	10675.05
Maximum	11191
Range	11191
Interquartile range (IQR)	5599.5

Descriptive statistics

Standard deviation	3246.662198
Coefficient of variation (CV)	0.5805739287
Kurtosis	-1.190028038
Mean	5592.159821
Median Absolute Deviation (MAD)	2791
Skewness	0.0398318728
Sum	12526438
Variance	10540815.43
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
5524	1	< 0.1%
6885	1	< 0.1%
3478	1	< 0.1%
7494	1	< 0.1%
1763	1	< 0.1%
7250	1	< 0.1%
2005	1	< 0.1%
10770	1	< 0.1%
2072	1	< 0.1%
9743	1	< 0.1%
Other values (2230)	2230	99.6%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	1	< 0.1%
1	1	< 0.1%
9	1	< 0.1%
13	1	< 0.1%
17	1	< 0.1%
20	1	< 0.1%
22	1	< 0.1%
24	1	< 0.1%
25	1	< 0.1%
35	1	< 0.1%

Value	Count	Frequency (%)
11191	1	< 0.1%
11188	1	< 0.1%
11187	1	< 0.1%
11181	1	< 0.1%
11178	1	< 0.1%
11176	1	< 0.1%
11171	1	< 0.1%
11166	1	< 0.1%
11148	1	< 0.1%
11133	1	< 0.1%

Year_Birth
Real number (ℝ_≥0)

Distinct	59
Distinct (%)	2.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1968.805804

Minimum	1893
Maximum	1996
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	17.6 KiB

Quantile statistics

Minimum	1893
5-th percentile	1950
Q1	1959
median	1970
Q3	1977
95-th percentile	1988
Maximum	1996
Range	103
Interquartile range (IQR)	18

Descriptive statistics

Standard deviation	11.98406946
Coefficient of variation (CV)	0.006086973858
Kurtosis	0.7174644425
Mean	1968.805804
Median Absolute Deviation (MAD)	9
Skewness	-0.3499438592
Sum	4410125
Variance	143.6179207
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1976	89	4.0%
1971	87	3.9%
1975	83	3.7%
1972	79	3.5%
1978	77	3.4%
1970	77	3.4%
1973	74	3.3%
1965	74	3.3%
1969	71	3.2%
1974	69	3.1%
Other values (49)	1460	65.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1893	1	< 0.1%
1899	1	< 0.1%
1900	1	< 0.1%
1940	1	< 0.1%
1941	1	< 0.1%
1943	7	0.3%
1944	7	0.3%
1945	8	0.4%
1946	16	0.7%
1947	16	0.7%

Value	Count	Frequency (%)
1996	2	0.1%
1995	5	0.2%
1994	3	0.1%
1993	5	0.2%
1992	13	0.6%
1991	15	0.7%
1990	18	0.8%
1989	30	1.3%
1988	29	1.3%
1987	27	1.2%

Education
Categorical

HIGH CORRELATION

Distinct	5
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Memory size	17.6 KiB

Graduation	1127
PhD	486
Master	370
2n Cycle	203
Basic	54

Length

Max length	10
Median length	10
Mean length	7.51875
Min length	3

Characters and Unicode

Total characters	16842
Distinct characters	22
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Graduation
2nd row	Graduation
3rd row	Graduation
4th row	Graduation
5th row	PhD

Common Values

Value	Count	Frequency (%)
Graduation	1127	50.3%
PhD	486	21.7%
Master	370	16.5%
2n Cycle	203	9.1%
Basic	54	2.4%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
graduation	1127	46.1%
phd	486	19.9%
master	370	15.1%
2n	203	8.3%
cycle	203	8.3%
basic	54	2.2%

Most occurring characters

Value	Count	Frequency (%)
a	2678	15.9%
r	1497	8.9%
t	1497	8.9%
n	1330	7.9%
i	1181	7.0%
G	1127	6.7%
d	1127	6.7%
u	1127	6.7%
o	1127	6.7%
e	573	3.4%
Other values (12)	3578	21.2%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	13710	81.4%
Uppercase Letter	2726	16.2%
Decimal Number	203	1.2%
Space Separator	203	1.2%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
a	2678	19.5%
r	1497	10.9%
t	1497	10.9%
n	1330	9.7%
i	1181	8.6%
d	1127	8.2%
u	1127	8.2%
o	1127	8.2%
e	573	4.2%
h	486	3.5%
Other values (4)	1087	7.9%

Uppercase Letter

Value	Count	Frequency (%)
G	1127	41.3%
P	486	17.8%
D	486	17.8%
M	370	13.6%
C	203	7.4%
B	54	2.0%

Decimal Number

Value	Count	Frequency (%)
2	203	100.0%

Space Separator

Value	Count	Frequency (%)
	203	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	16436	97.6%
Common	406	2.4%

Most frequent character per script

Latin

Value	Count	Frequency (%)
a	2678	16.3%
r	1497	9.1%
t	1497	9.1%
n	1330	8.1%
i	1181	7.2%
G	1127	6.9%
d	1127	6.9%
u	1127	6.9%
o	1127	6.9%
e	573	3.5%
Other values (10)	3172	19.3%

Common

Value	Count	Frequency (%)
2	203	50.0%
	203	50.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	16842	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
a	2678	15.9%
r	1497	8.9%
t	1497	8.9%
n	1330	7.9%
i	1181	7.0%
G	1127	6.7%
d	1127	6.7%
u	1127	6.7%
o	1127	6.7%
e	573	3.4%
Other values (12)	3578	21.2%

Marital_Status
Categorical

HIGH CORRELATION

Distinct	8
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Memory size	17.6 KiB

Married	864
Together	580
Single	480
Divorced	232
Widow	77
Other values (3)	7

Length

Max length	8
Median length	7
Mean length	7.073214286
Min length	4

Characters and Unicode

Total characters	15844
Distinct characters	26
Distinct categories	2 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Single
2nd row	Single
3rd row	Together
4th row	Together
5th row	Married

Common Values

Value	Count	Frequency (%)
Married	864	38.6%
Together	580	25.9%
Single	480	21.4%
Divorced	232	10.4%
Widow	77	3.4%
Alone	3	0.1%
Absurd	2	0.1%
YOLO	2	0.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
married	864	38.6%
together	580	25.9%
single	480	21.4%
divorced	232	10.4%
widow	77	3.4%
alone	3	0.1%
absurd	2	0.1%
yolo	2	0.1%

Most occurring characters

Value	Count	Frequency (%)
e	2739	17.3%
r	2542	16.0%
i	1653	10.4%
d	1175	7.4%
g	1060	6.7%
o	892	5.6%
M	864	5.5%
a	864	5.5%
T	580	3.7%
t	580	3.7%
Other values (16)	2895	18.3%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	13598	85.8%
Uppercase Letter	2246	14.2%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	2739	20.1%
r	2542	18.7%
i	1653	12.2%
d	1175	8.6%
g	1060	7.8%
o	892	6.6%
a	864	6.4%
t	580	4.3%
h	580	4.3%
n	483	3.6%
Other values (7)	1030	7.6%

Uppercase Letter

Value	Count	Frequency (%)
M	864	38.5%
T	580	25.8%
S	480	21.4%
D	232	10.3%
W	77	3.4%
A	5	0.2%
O	4	0.2%
Y	2	0.1%
L	2	0.1%

Most occurring scripts

Value	Count	Frequency (%)
Latin	15844	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	2739	17.3%
r	2542	16.0%
i	1653	10.4%
d	1175	7.4%
g	1060	6.7%
o	892	5.6%
M	864	5.5%
a	864	5.5%
T	580	3.7%
t	580	3.7%
Other values (16)	2895	18.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	15844	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	2739	17.3%
r	2542	16.0%
i	1653	10.4%
d	1175	7.4%
g	1060	6.7%
o	892	5.6%
M	864	5.5%
a	864	5.5%
T	580	3.7%
t	580	3.7%
Other values (16)	2895	18.3%

Income
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct	1974
Distinct (%)	89.1%
Missing	24
Missing (%)	1.1%
Infinite	0
Infinite (%)	0.0%
Mean	52247.25135

Minimum	1730
Maximum	666666
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	17.6 KiB

Quantile statistics

Minimum	1730
5-th percentile	18985.5
Q1	35303
median	51381.5
Q3	68522
95-th percentile	84130
Maximum	666666
Range	664936
Interquartile range (IQR)	33219

Descriptive statistics

Standard deviation	25173.07666
Coefficient of variation (CV)	0.4818067173
Kurtosis	159.6366996
Mean	52247.25135
Median Absolute Deviation (MAD)	16557.5
Skewness	6.763487373
Sum	115779909
Variance	633683788.6
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
7500	12	0.5%
35860	4	0.2%
37760	3	0.1%
83844	3	0.1%
63841	3	0.1%
18929	3	0.1%
47025	3	0.1%
34176	3	0.1%
48432	3	0.1%
80134	3	0.1%
Other values (1964)	2176	97.1%
(Missing)	24	1.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1730	1	< 0.1%
2447	1	< 0.1%
3502	1	< 0.1%
4023	1	< 0.1%
4428	1	< 0.1%
4861	1	< 0.1%
5305	1	< 0.1%
5648	1	< 0.1%
6560	1	< 0.1%
6835	1	< 0.1%

Value	Count	Frequency (%)
666666	1	< 0.1%
162397	1	< 0.1%
160803	1	< 0.1%
157733	1	< 0.1%
157243	1	< 0.1%
157146	1	< 0.1%
156924	1	< 0.1%
153924	1	< 0.1%
113734	1	< 0.1%
105471	1	< 0.1%

Kidhome
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	3
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	17.6 KiB

0	1293
1	899
2	48

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	2240
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	1
3rd row	0
4th row	1
5th row	1

Common Values

Value	Count	Frequency (%)
0	1293	57.7%
1	899	40.1%
2	48	2.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	1293	57.7%
1	899	40.1%
2	48	2.1%

Most occurring characters

Value	Count	Frequency (%)
0	1293	57.7%
1	899	40.1%
2	48	2.1%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	2240	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	1293	57.7%
1	899	40.1%
2	48	2.1%

Most occurring scripts

Value	Count	Frequency (%)
Common	2240	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	1293	57.7%
1	899	40.1%
2	48	2.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2240	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	1293	57.7%
1	899	40.1%
2	48	2.1%

Teenhome
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	3
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	17.6 KiB

0	1158
1	1030
2	52

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	2240
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	1
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	1158	51.7%
1	1030	46.0%
2	52	2.3%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	1158	51.7%
1	1030	46.0%
2	52	2.3%

Most occurring characters

Value	Count	Frequency (%)
0	1158	51.7%
1	1030	46.0%
2	52	2.3%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	2240	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	1158	51.7%
1	1030	46.0%
2	52	2.3%

Most occurring scripts

Value	Count	Frequency (%)
Common	2240	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	1158	51.7%
1	1030	46.0%
2	52	2.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2240	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	1158	51.7%
1	1030	46.0%
2	52	2.3%

Dt_Customer
Categorical

HIGH CARDINALITY

Distinct	663
Distinct (%)	29.6%
Missing	0
Missing (%)	0.0%
Memory size	17.6 KiB

31-08-2012	12
12-09-2012	11
14-02-2013	11
12-05-2014	11
20-08-2013	10
Other values (658)	2185

Length

Max length	10
Median length	10
Mean length	10
Min length	10

Characters and Unicode

Total characters	22400
Distinct characters	11
Distinct categories	2 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	107 ?
Unique (%)	4.8%

Sample

1st row	04-09-2012
2nd row	08-03-2014
3rd row	21-08-2013
4th row	10-02-2014
5th row	19-01-2014

Common Values

Value	Count	Frequency (%)
31-08-2012	12	0.5%
12-09-2012	11	0.5%
14-02-2013	11	0.5%
12-05-2014	11	0.5%
20-08-2013	10	0.4%
22-05-2014	10	0.4%
05-04-2014	9	0.4%
23-03-2014	9	0.4%
02-01-2013	9	0.4%
01-03-2014	9	0.4%
Other values (653)	2139	95.5%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
31-08-2012	12	0.5%
12-09-2012	11	0.5%
14-02-2013	11	0.5%
12-05-2014	11	0.5%
20-08-2013	10	0.4%
22-05-2014	10	0.4%
05-04-2014	9	0.4%
23-03-2014	9	0.4%
02-01-2013	9	0.4%
01-03-2014	9	0.4%
Other values (653)	2139	95.5%

Most occurring characters

Value	Count	Frequency (%)
0	4980	22.2%
-	4480	20.0%
1	4228	18.9%
2	4076	18.2%
3	1742	7.8%
4	928	4.1%
8	445	2.0%
5	424	1.9%
9	403	1.8%
6	363	1.6%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	17920	80.0%
Dash Punctuation	4480	20.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	4980	27.8%
1	4228	23.6%
2	4076	22.7%
3	1742	9.7%
4	928	5.2%
8	445	2.5%
5	424	2.4%
9	403	2.2%
6	363	2.0%
7	331	1.8%

Dash Punctuation

Value	Count	Frequency (%)
-	4480	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	22400	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	4980	22.2%
-	4480	20.0%
1	4228	18.9%
2	4076	18.2%
3	1742	7.8%
4	928	4.1%
8	445	2.0%
5	424	1.9%
9	403	1.8%
6	363	1.6%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	22400	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	4980	22.2%
-	4480	20.0%
1	4228	18.9%
2	4076	18.2%
3	1742	7.8%
4	928	4.1%
8	445	2.0%
5	424	1.9%
9	403	1.8%
6	363	1.6%

Recency
Real number (ℝ_≥0)

ZEROS

Distinct	100
Distinct (%)	4.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	49.109375

Minimum	0
Maximum	99
Zeros	28
Zeros (%)	1.2%
Negative	0
Negative (%)	0.0%
Memory size	17.6 KiB

Quantile statistics

Minimum	0
5-th percentile	4
Q1	24
median	49
Q3	74
95-th percentile	94
Maximum	99
Range	99
Interquartile range (IQR)	50

Descriptive statistics

Standard deviation	28.96245281
Coefficient of variation (CV)	0.5897540502
Kurtosis	-1.201896799
Mean	49.109375
Median Absolute Deviation (MAD)	25
Skewness	-0.001986658634
Sum	110005
Variance	838.8236727
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
56	37	1.7%
30	32	1.4%
54	32	1.4%
46	31	1.4%
92	30	1.3%
49	30	1.3%
65	30	1.3%
3	29	1.3%
29	29	1.3%
71	29	1.3%
Other values (90)	1931	86.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	28	1.2%
1	24	1.1%
2	28	1.2%
3	29	1.3%
4	27	1.2%
5	15	0.7%
6	21	0.9%
7	12	0.5%
8	25	1.1%
9	24	1.1%

Value	Count	Frequency (%)
99	17	0.8%
98	22	1.0%
97	20	0.9%
96	25	1.1%
95	19	0.8%
94	26	1.2%
93	21	0.9%
92	30	1.3%
91	18	0.8%
90	20	0.9%

MntWines
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	776
Distinct (%)	34.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	303.9357143

Minimum	0
Maximum	1493
Zeros	13
Zeros (%)	0.6%
Negative	0
Negative (%)	0.0%
Memory size	17.6 KiB

Quantile statistics

Minimum	0
5-th percentile	3
Q1	23.75
median	173.5
Q3	504.25
95-th percentile	1000
Maximum	1493
Range	1493
Interquartile range (IQR)	480.5

Descriptive statistics

Standard deviation	336.5973926
Coefficient of variation (CV)	1.107462456
Kurtosis	0.5987435935
Mean	303.9357143
Median Absolute Deviation (MAD)	164.5
Skewness	1.175770564
Sum	680816
Variance	113297.8047
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
2	42	1.9%
5	40	1.8%
1	37	1.7%
6	37	1.7%
4	33	1.5%
8	30	1.3%
3	30	1.3%
9	28	1.2%
12	25	1.1%
10	24	1.1%
Other values (766)	1914	85.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	13	0.6%
1	37	1.7%
2	42	1.9%
3	30	1.3%
4	33	1.5%
5	40	1.8%
6	37	1.7%
7	22	1.0%
8	30	1.3%
9	28	1.2%

Value	Count	Frequency (%)
1493	1	< 0.1%
1492	2	0.1%
1486	1	< 0.1%
1478	2	0.1%
1462	1	< 0.1%
1459	1	< 0.1%
1449	1	< 0.1%
1396	1	< 0.1%
1394	1	< 0.1%
1379	1	< 0.1%

MntFruits
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	158
Distinct (%)	7.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	26.30223214

Minimum	0
Maximum	199
Zeros	400
Zeros (%)	17.9%
Negative	0
Negative (%)	0.0%
Memory size	17.6 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	1
median	8
Q3	33
95-th percentile	123
Maximum	199
Range	199
Interquartile range (IQR)	32

Descriptive statistics

Standard deviation	39.77343376
Coefficient of variation (CV)	1.51216952
Kurtosis	4.050976251
Mean	26.30223214
Median Absolute Deviation (MAD)	8
Skewness	2.102063305
Sum	58917
Variance	1581.926033
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	400	17.9%
1	162	7.2%
2	120	5.4%
3	116	5.2%
4	104	4.6%
7	67	3.0%
5	65	2.9%
6	62	2.8%
12	50	2.2%
8	48	2.1%
Other values (148)	1046	46.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	400	17.9%
1	162	7.2%
2	120	5.4%
3	116	5.2%
4	104	4.6%
5	65	2.9%
6	62	2.8%
7	67	3.0%
8	48	2.1%
9	35	1.6%

Value	Count	Frequency (%)
199	2	0.1%
197	1	< 0.1%
194	3	0.1%
193	2	0.1%
190	1	< 0.1%
189	1	< 0.1%
185	2	0.1%
184	1	< 0.1%
183	3	0.1%
181	1	< 0.1%

MntMeatProducts
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	558
Distinct (%)	24.9%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	166.95

Minimum	0
Maximum	1725
Zeros	1
Zeros (%)	< 0.1%
Negative	0
Negative (%)	0.0%
Memory size	17.6 KiB

Quantile statistics

Minimum	0
5-th percentile	4
Q1	16
median	67
Q3	232
95-th percentile	687.1
Maximum	1725
Range	1725
Interquartile range (IQR)	216

Descriptive statistics

Standard deviation	225.7153725
Coefficient of variation (CV)	1.351993846
Kurtosis	5.516724101
Mean	166.95
Median Absolute Deviation (MAD)	59
Skewness	2.083233113
Sum	373968
Variance	50947.42939
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
7	53	2.4%
5	50	2.2%
11	49	2.2%
8	46	2.1%
6	43	1.9%
10	40	1.8%
3	40	1.8%
9	38	1.7%
16	36	1.6%
12	35	1.6%
Other values (548)	1810	80.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	1	< 0.1%
1	14	0.6%
2	30	1.3%
3	40	1.8%
4	30	1.3%
5	50	2.2%
6	43	1.9%
7	53	2.4%
8	46	2.1%
9	38	1.7%

Value	Count	Frequency (%)
1725	2	0.1%
1622	1	< 0.1%
1607	1	< 0.1%
1582	1	< 0.1%
984	1	< 0.1%
981	1	< 0.1%
974	1	< 0.1%
968	1	< 0.1%
961	1	< 0.1%
951	2	0.1%

MntFishProducts
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	182
Distinct (%)	8.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	37.52544643

Minimum	0
Maximum	259
Zeros	384
Zeros (%)	17.1%
Negative	0
Negative (%)	0.0%
Memory size	17.6 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	3
median	12
Q3	50
95-th percentile	168.05
Maximum	259
Range	259
Interquartile range (IQR)	47

Descriptive statistics

Standard deviation	54.6289794
Coefficient of variation (CV)	1.45578493
Kurtosis	3.096460912
Mean	37.52544643
Median Absolute Deviation (MAD)	12
Skewness	1.919768971
Sum	84057
Variance	2984.325391
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	384	17.1%
2	156	7.0%
3	130	5.8%
4	108	4.8%
6	82	3.7%
7	66	2.9%
8	58	2.6%
10	55	2.5%
13	48	2.1%
12	47	2.1%
Other values (172)	1106	49.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	384	17.1%
1	10	0.4%
2	156	7.0%
3	130	5.8%
4	108	4.8%
5	1	< 0.1%
6	82	3.7%
7	66	2.9%
8	58	2.6%
10	55	2.5%

Value	Count	Frequency (%)
259	1	< 0.1%
258	3	0.1%
254	1	< 0.1%
253	1	< 0.1%
250	3	0.1%
247	1	< 0.1%
246	1	< 0.1%
242	1	< 0.1%
240	2	0.1%
237	2	0.1%

MntSweetProducts
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	177
Distinct (%)	7.9%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	27.06294643

Minimum	0
Maximum	263
Zeros	419
Zeros (%)	18.7%
Negative	0
Negative (%)	0.0%
Memory size	17.6 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	1
median	8
Q3	33
95-th percentile	126
Maximum	263
Range	263
Interquartile range (IQR)	32

Descriptive statistics

Standard deviation	41.28049849
Coefficient of variation (CV)	1.525351225
Kurtosis	4.376548261
Mean	27.06294643
Median Absolute Deviation (MAD)	8
Skewness	2.136080712
Sum	60621
Variance	1704.079555
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	419	18.7%
1	161	7.2%
2	128	5.7%
3	101	4.5%
4	82	3.7%
5	65	2.9%
6	64	2.9%
7	57	2.5%
8	56	2.5%
12	45	2.0%
Other values (167)	1062	47.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	419	18.7%
1	161	7.2%
2	128	5.7%
3	101	4.5%
4	82	3.7%
5	65	2.9%
6	64	2.9%
7	57	2.5%
8	56	2.5%
9	42	1.9%

Value	Count	Frequency (%)
263	1	< 0.1%
262	1	< 0.1%
198	1	< 0.1%
197	1	< 0.1%
196	1	< 0.1%
195	1	< 0.1%
194	3	0.1%
192	3	0.1%
191	1	< 0.1%
189	2	0.1%

MntGoldProds
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	213
Distinct (%)	9.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	44.021875

Minimum	0
Maximum	362
Zeros	61
Zeros (%)	2.7%
Negative	0
Negative (%)	0.0%
Memory size	17.6 KiB

Quantile statistics

Minimum	0
5-th percentile	1
Q1	9
median	24
Q3	56
95-th percentile	165.05
Maximum	362
Range	362
Interquartile range (IQR)	47

Descriptive statistics

Standard deviation	52.16743891
Coefficient of variation (CV)	1.185034461
Kurtosis	3.55170925
Mean	44.021875
Median Absolute Deviation (MAD)	18
Skewness	1.886105609
Sum	98609
Variance	2721.441683
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1	73	3.3%
4	70	3.1%
3	69	3.1%
5	63	2.8%
12	63	2.8%
2	62	2.8%
0	61	2.7%
6	57	2.5%
7	54	2.4%
10	49	2.2%
Other values (203)	1619	72.3%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	61	2.7%
1	73	3.3%
2	62	2.8%
3	69	3.1%
4	70	3.1%
5	63	2.8%
6	57	2.5%
7	54	2.4%
8	40	1.8%
9	44	2.0%

Value	Count	Frequency (%)
362	1	< 0.1%
321	1	< 0.1%
291	1	< 0.1%
262	1	< 0.1%
249	1	< 0.1%
248	1	< 0.1%
247	1	< 0.1%
246	1	< 0.1%
245	1	< 0.1%
242	2	0.1%

NumDealsPurchases
Real number (ℝ_≥0)

HIGH CORRELATION
ZEROS

Distinct	15
Distinct (%)	0.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2.325

Minimum	0
Maximum	15
Zeros	46
Zeros (%)	2.1%
Negative	0
Negative (%)	0.0%
Memory size	17.6 KiB

Quantile statistics

Minimum	0
5-th percentile	1
Q1	1
median	2
Q3	3
95-th percentile	6
Maximum	15
Range	15
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	1.932237501
Coefficient of variation (CV)	0.8310698928
Kurtosis	8.936914321
Mean	2.325
Median Absolute Deviation (MAD)	1
Skewness	2.418569388
Sum	5208
Variance	3.73354176
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=15)

Value	Count	Frequency (%)
1	970	43.3%
2	497	22.2%
3	297	13.3%
4	189	8.4%
5	94	4.2%
6	61	2.7%
0	46	2.1%
7	40	1.8%
8	14	0.6%
9	8	0.4%
Other values (5)	24	1.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	46	2.1%
1	970	43.3%
2	497	22.2%
3	297	13.3%
4	189	8.4%
5	94	4.2%
6	61	2.7%
7	40	1.8%
8	14	0.6%
9	8	0.4%

Value	Count	Frequency (%)
15	7	0.3%
13	3	0.1%
12	4	0.2%
11	5	0.2%
10	5	0.2%
9	8	0.4%
8	14	0.6%
7	40	1.8%
6	61	2.7%
5	94	4.2%

NumWebPurchases
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	15
Distinct (%)	0.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	4.084821429

Minimum	0
Maximum	27
Zeros	49
Zeros (%)	2.2%
Negative	0
Negative (%)	0.0%
Memory size	17.6 KiB

Quantile statistics

Minimum	0
5-th percentile	1
Q1	2
median	4
Q3	6
95-th percentile	9
Maximum	27
Range	27
Interquartile range (IQR)	4

Descriptive statistics

Standard deviation	2.778714147
Coefficient of variation (CV)	0.680253518
Kurtosis	5.703128364
Mean	4.084821429
Median Absolute Deviation (MAD)	2
Skewness	1.382794296
Sum	9150
Variance	7.721252313
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=15)

Value	Count	Frequency (%)
2	373	16.7%
1	354	15.8%
3	336	15.0%
4	280	12.5%
5	220	9.8%
6	205	9.2%
7	155	6.9%
8	102	4.6%
9	75	3.3%
0	49	2.2%
Other values (5)	91	4.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	49	2.2%
1	354	15.8%
2	373	16.7%
3	336	15.0%
4	280	12.5%
5	220	9.8%
6	205	9.2%
7	155	6.9%
8	102	4.6%
9	75	3.3%

Value	Count	Frequency (%)
27	2	0.1%
25	1	< 0.1%
23	1	< 0.1%
11	44	2.0%
10	43	1.9%
9	75	3.3%
8	102	4.6%
7	155	6.9%
6	205	9.2%
5	220	9.8%

NumCatalogPurchases
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	14
Distinct (%)	0.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2.662053571

Minimum	0
Maximum	28
Zeros	586
Zeros (%)	26.2%
Negative	0
Negative (%)	0.0%
Memory size	17.6 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	2
Q3	4
95-th percentile	9
Maximum	28
Range	28
Interquartile range (IQR)	4

Descriptive statistics

Standard deviation	2.923100656
Coefficient of variation (CV)	1.098062296
Kurtosis	8.047436789
Mean	2.662053571
Median Absolute Deviation (MAD)	2
Skewness	1.880988778
Sum	5963
Variance	8.544517442
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=14)

Value	Count	Frequency (%)
0	586	26.2%
1	497	22.2%
2	276	12.3%
3	184	8.2%
4	182	8.1%
5	140	6.2%
6	128	5.7%
7	79	3.5%
8	55	2.5%
10	48	2.1%
Other values (4)	65	2.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	586	26.2%
1	497	22.2%
2	276	12.3%
3	184	8.2%
4	182	8.1%
5	140	6.2%
6	128	5.7%
7	79	3.5%
8	55	2.5%
9	42	1.9%

Value	Count	Frequency (%)
28	3	0.1%
22	1	< 0.1%
11	19	0.8%
10	48	2.1%
9	42	1.9%
8	55	2.5%
7	79	3.5%
6	128	5.7%
5	140	6.2%
4	182	8.1%

NumStorePurchases
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	14
Distinct (%)	0.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	5.790178571

Minimum	0
Maximum	13
Zeros	15
Zeros (%)	0.7%
Negative	0
Negative (%)	0.0%
Memory size	17.6 KiB

Quantile statistics

Minimum	0
5-th percentile	2
Q1	3
median	5
Q3	8
95-th percentile	12
Maximum	13
Range	13
Interquartile range (IQR)	5

Descriptive statistics

Standard deviation	3.250958146
Coefficient of variation (CV)	0.5614607746
Kurtosis	-0.6220482771
Mean	5.790178571
Median Absolute Deviation (MAD)	2
Skewness	0.7022372855
Sum	12970
Variance	10.56872886
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=14)

Value	Count	Frequency (%)
3	490	21.9%
4	323	14.4%
2	223	10.0%
5	212	9.5%
6	178	7.9%
8	149	6.7%
7	143	6.4%
10	125	5.6%
9	106	4.7%
12	105	4.7%
Other values (4)	186	8.3%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	15	0.7%
1	7	0.3%
2	223	10.0%
3	490	21.9%
4	323	14.4%
5	212	9.5%
6	178	7.9%
7	143	6.4%
8	149	6.7%
9	106	4.7%

Value	Count	Frequency (%)
13	83	3.7%
12	105	4.7%
11	81	3.6%
10	125	5.6%
9	106	4.7%
8	149	6.7%
7	143	6.4%
6	178	7.9%
5	212	9.5%
4	323	14.4%

NumWebVisitsMonth
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	16
Distinct (%)	0.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	5.316517857

Minimum	0
Maximum	20
Zeros	11
Zeros (%)	0.5%
Negative	0
Negative (%)	0.0%
Memory size	17.6 KiB

Quantile statistics

Minimum	0
5-th percentile	1
Q1	3
median	6
Q3	7
95-th percentile	8
Maximum	20
Range	20
Interquartile range (IQR)	4

Descriptive statistics

Standard deviation	2.42664501
Coefficient of variation (CV)	0.4564350341
Kurtosis	1.821613827
Mean	5.316517857
Median Absolute Deviation (MAD)	2
Skewness	0.2079255568
Sum	11909
Variance	5.888606002
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=16)

Value	Count	Frequency (%)
7	393	17.5%
8	342	15.3%
6	340	15.2%
5	281	12.5%
4	218	9.7%
3	205	9.2%
2	202	9.0%
1	153	6.8%
9	83	3.7%
0	11	0.5%
Other values (6)	12	0.5%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	11	0.5%
1	153	6.8%
2	202	9.0%
3	205	9.2%
4	218	9.7%
5	281	12.5%
6	340	15.2%
7	393	17.5%
8	342	15.3%
9	83	3.7%

Value	Count	Frequency (%)
20	3	0.1%
19	2	0.1%
17	1	< 0.1%
14	2	0.1%
13	1	< 0.1%
10	3	0.1%
9	83	3.7%
8	342	15.3%
7	393	17.5%
6	340	15.2%

AcceptedCmp3
Categorical

HIGH CORRELATION

Distinct	2
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	17.6 KiB

0	2077
1	163

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	2240
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	2077	92.7%
1	163	7.3%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	2077	92.7%
1	163	7.3%

Most occurring characters

Value	Count	Frequency (%)
0	2077	92.7%
1	163	7.3%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	2240	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	2077	92.7%
1	163	7.3%

Most occurring scripts

Value	Count	Frequency (%)
Common	2240	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	2077	92.7%
1	163	7.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2240	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	2077	92.7%
1	163	7.3%

AcceptedCmp4
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	17.6 KiB

0	2073
1	167

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	2240
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	2073	92.5%
1	167	7.5%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	2073	92.5%
1	167	7.5%

Most occurring characters

Value	Count	Frequency (%)
0	2073	92.5%
1	167	7.5%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	2240	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	2073	92.5%
1	167	7.5%

Most occurring scripts

Value	Count	Frequency (%)
Common	2240	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	2073	92.5%
1	167	7.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2240	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	2073	92.5%
1	167	7.5%

AcceptedCmp5
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	17.6 KiB

0	2077
1	163

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	2240
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	2077	92.7%
1	163	7.3%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	2077	92.7%
1	163	7.3%

Most occurring characters

Value	Count	Frequency (%)
0	2077	92.7%
1	163	7.3%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	2240	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	2077	92.7%
1	163	7.3%

Most occurring scripts

Value	Count	Frequency (%)
Common	2240	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	2077	92.7%
1	163	7.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2240	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	2077	92.7%
1	163	7.3%

AcceptedCmp1
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	17.6 KiB

0	2096
1	144

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	2240
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	2096	93.6%
1	144	6.4%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	2096	93.6%
1	144	6.4%

Most occurring characters

Value	Count	Frequency (%)
0	2096	93.6%
1	144	6.4%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	2240	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	2096	93.6%
1	144	6.4%

Most occurring scripts

Value	Count	Frequency (%)
Common	2240	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	2096	93.6%
1	144	6.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2240	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	2096	93.6%
1	144	6.4%

AcceptedCmp2
Categorical

HIGH CORRELATION

Distinct	2
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	17.6 KiB

0	2210
1	30

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	2240
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	2210	98.7%
1	30	1.3%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	2210	98.7%
1	30	1.3%

Most occurring characters

Value	Count	Frequency (%)
0	2210	98.7%
1	30	1.3%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	2240	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	2210	98.7%
1	30	1.3%

Most occurring scripts

Value	Count	Frequency (%)
Common	2240	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	2210	98.7%
1	30	1.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2240	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	2210	98.7%
1	30	1.3%

Complain
Categorical

HIGH CORRELATION

Distinct	2
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	17.6 KiB

0	2219
1	21

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	2240
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	2219	99.1%
1	21	0.9%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	2219	99.1%
1	21	0.9%

Most occurring characters

Value	Count	Frequency (%)
0	2219	99.1%
1	21	0.9%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	2240	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	2219	99.1%
1	21	0.9%

Most occurring scripts

Value	Count	Frequency (%)
Common	2240	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	2219	99.1%
1	21	0.9%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2240	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	2219	99.1%
1	21	0.9%

Z_CostContact
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct	1
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	17.6 KiB

3	2240

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	2240
Distinct characters	1
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	3
2nd row	3
3rd row	3
4th row	3
5th row	3

Common Values

Value	Count	Frequency (%)
3	2240	100.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
3	2240	100.0%

Most occurring characters

Value	Count	Frequency (%)
3	2240	100.0%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	2240	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
3	2240	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	2240	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
3	2240	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2240	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
3	2240	100.0%

Z_Revenue
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct	1
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	17.6 KiB

11	2240

Length

Max length	2
Median length	2
Mean length	2
Min length	2

Characters and Unicode

Total characters	4480
Distinct characters	1
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	11
2nd row	11
3rd row	11
4th row	11
5th row	11

Common Values

Value	Count	Frequency (%)
11	2240	100.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
11	2240	100.0%

Most occurring characters

Value	Count	Frequency (%)
1	4480	100.0%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	4480	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
1	4480	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	4480	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
1	4480	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	4480	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
1	4480	100.0%

Response
Categorical

HIGH CORRELATION

Distinct	2
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	17.6 KiB

0	1906
1	334

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	2240
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	1906	85.1%
1	334	14.9%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	1906	85.1%
1	334	14.9%

Most occurring characters

Value	Count	Frequency (%)
0	1906	85.1%
1	334	14.9%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	2240	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	1906	85.1%
1	334	14.9%

Most occurring scripts

Value	Count	Frequency (%)
Common	2240	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	1906	85.1%
1	334	14.9%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2240	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	1906	85.1%
1	334	14.9%

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

First rows

	ID	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Dt_Customer	Recency	MntWines	MntFruits	MntMeatProducts	MntFishProducts	MntSweetProducts	MntGoldProds	NumDealsPurchases	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	AcceptedCmp3	Z_CostContact	Z_Revenue	Response
0	5524	1957	Graduation	Single	58138.0	0	0	04-09-2012	58	635	88	546	172	88	88	3	8	10	4	7	0	3	11	1
1	2174	1954	Graduation	Single	46344.0	1	1	08-03-2014	38	11	1	6	2	1	6	2	1	1	2	5	0	3	11	0
2	4141	1965	Graduation	Together	71613.0	0	0	21-08-2013	26	426	49	127	111	21	42	1	8	2	10	4	0	3	11	0
3	6182	1984	Graduation	Together	26646.0	1	0	10-02-2014	26	11	4	20	10	3	5	2	2	0	4	6	0	3	11	0
4	5324	1981	PhD	Married	58293.0	1	0	19-01-2014	94	173	43	118	46	27	15	5	5	3	6	5	0	3	11	0
5	7446	1967	Master	Together	62513.0	0	1	09-09-2013	16	520	42	98	0	42	14	2	6	4	10	6	0	3	11	0
6	965	1971	Graduation	Divorced	55635.0	0	1	13-11-2012	34	235	65	164	50	49	27	4	7	3	7	6	0	3	11	0
7	6177	1985	PhD	Married	33454.0	1	0	08-05-2013	32	76	10	56	3	1	23	2	4	0	4	8	0	3	11	0
8	4855	1974	PhD	Together	30351.0	1	0	06-06-2013	19	14	0	24	3	3	2	1	3	0	2	9	0	3	11	1
9	5899	1950	PhD	Together	5648.0	1	1	13-03-2014	68	28	0	6	1	1	13	1	1	0	0	20	1	3	11	0

Last rows

	ID	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Dt_Customer	Recency	MntWines	MntFruits	MntMeatProducts	MntFishProducts	MntSweetProducts	MntGoldProds	NumDealsPurchases	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	AcceptedCmp3	AcceptedCmp4	AcceptedCmp1	Z_CostContact	Z_Revenue	Response
2230	7004	1984	Graduation	Single	11012.0	1	0	16-03-2013	82	24	3	26	7	1	23	3	3	1	2	9	1	0	0	3	11	0
2231	9817	1970	Master	Single	44802.0	0	0	21-08-2012	71	853	10	143	13	10	20	2	9	4	12	8	0	0	0	3	11	0
2232	8080	1986	Graduation	Single	26816.0	0	0	17-08-2012	50	5	1	6	3	4	3	1	0	0	3	4	0	0	0	3	11	0
2233	9432	1977	Graduation	Together	666666.0	1	0	02-06-2013	23	9	14	18	8	1	12	4	3	1	3	6	0	0	0	3	11	0
2234	8372	1974	Graduation	Married	34421.0	1	0	01-07-2013	81	3	3	7	6	2	9	1	1	0	2	7	0	0	0	3	11	0
2235	10870	1967	Graduation	Married	61223.0	0	1	13-06-2013	46	709	43	182	42	118	247	2	9	3	4	5	0	0	0	3	11	0
2236	4001	1946	PhD	Together	64014.0	2	1	10-06-2014	56	406	0	30	0	0	8	7	8	2	5	7	0	0	1	3	11	0
2237	7270	1981	Graduation	Divorced	56981.0	0	0	25-01-2014	91	908	48	217	32	12	24	1	2	3	13	6	0	1	0	3	11	0
2238	8235	1956	Master	Together	69245.0	0	1	24-01-2014	8	428	30	214	80	30	61	2	6	5	10	3	0	0	0	3	11	0
2239	9405	1954	PhD	Married	52869.0	1	1	15-10-2012	40	84	3	61	2	1	21	3	3	1	4	7	0	0	0	3	11	1

Overview

Variables

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Uppercase Letter

Decimal Number

Space Separator

Most occurring scripts

Most frequent character per script

Latin

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Uppercase Letter

Most occurring scripts

Most frequent character per script

Latin

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Dash Punctuation

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts